class_docstr: Adds TensorFlow specific functions.
docstr: Get loss function corresponding to the keyword.
docstr_tok: ["Get","loss","function","corresponding","to","the","keyword","."]
code: def get_loss(self, dist_type):
      """Get loss function corresponding to the keyword."""
      if dist_type == "binary":
          # Use sigmoid as final layer
          return tensorflow.keras.losses.BinaryCrossentropy(
              from_logits=False
          )
      elif dist_type == "category":
          # User softmax as final layer
          return tensorflow.keras.losses.CategoricalCrossentropy(
              from_logits=False
          )
      elif dist_type == "continuous":
          return tensorflow.keras.losses.MeanSquaredError()
      super(TensorflowEngine, self).get_loss(dist_type)
  
file_path: fairlearn/fairlearn/adversarial/_tensorflow_engine.py
imports: from ._backend_engine import BackendEngine
  from numpy import finfo, float32
label_desc: Use image node_id 5 for calling the TensorflowEngine obj's underlying member method code with example usage: obj.get_loss(dist_type) and returns: tensorflow, tensorflow, tensorflow